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7th International Conference on Photonics and Imaging in Biology and Medicine

作者： Yangguang, Sun Tian, Lan Xiaowei, Fu Mingyue, Ding Institute of Pattern Recognition and Artificial Intelligence Image Processing and Intelligence Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan 430074 China School of Life Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780819475398

In this paper we proposed a novel path integral method using multilevel Metropolis sampling to extract the contours of interested objects of medical images, which is a quantum statistical approach inspired by the essential characteristics of quantum. The implemented multilevel Metropolis sampling is a fast approach to the sampling problem and can be used to sample any distribution function and calculate all actions for the movements of quantum particle towards the position with high probability density likely to be accepted. Comparing with traditional deformable models based on classical mechanics which are sensitive to the initialization of contour and difficult to adapt topology changes of the contour, our work has the capability to break through the limitations of deformable models and makes the boundary extraction in high noise and low contrast of medical images possible. Experiments were performed with synthetic and medical images and the feasibility and potential of our method was demonstrated. © 2009 SPIE.

关键词： Distribution functions

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On-line fingerprint verification

On-line fingerprint verification

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International Conference on pattern recognition

作者： A. Jain Lin Hong Pattern Recognition and Image Processing Laboratory Department of Computer Science Michigan State University East Lansing MI USA

We describe the design and implementation of an online fingerprint verification system which operates in two stages: (i) minutia extraction and (ii) minutia matching. An improved minutia extraction algorithm that is much faster and more accurate than our earlier algorithm has been implemented. For minutia matching, an alignment-based elastic matching algorithm has been developed. This algorithm is capable of finding the correspondences between input minutiae and the stored template without resorting to exhaustive search and has the ability to adaptively compensate for the nonlinear deformations and inexact pose transformations between finger prints. The system has been tested on two sets of finger print images captured with inkless scanners. The verification accuracy is found to be over 99% with a 15% reject rate. Typically, a complete fingerprint verification procedure takes, on an average, about 8 seconds on a SPARC 20 workstation. It meets the response time requirements of on-line verification with high accuracy.

关键词： Fingerprint recognition Access control Feature extraction Flowcharts pattern recognition image processing Laboratories Computer science pattern matching System testing

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Page segmentation using texture discrimination masks

Page segmentation using texture discrimination masks

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IEEE International Conference on image processing

作者： A.K. Jain Y. Zhong Pattern Recognition and Image Processing Laboratory Department of Computer Science Michigan State University East Lansing MI USA

We propose a new texture-based page segmentation algorithm which automatically extracts the text, halftone, and line-drawing regions from input greyscale document images. This approach utilizes a neural network to train a set of masks which is optimal for discriminating the three main texture classes in the page segmentation problem: halftone, background, and text and line-drawing regions. The test and line-drawing regions are further discriminated based on connectivity analysis. We have applied the algorithm to successfully segment English and Chinese document images. We also demonstrate that the masks can perform language separation (English/Chinese) when appropriately trained.

关键词： image segmentation Neural networks Gabor filters Natural languages image analysis Multi-layer neural network image texture analysis pattern recognition image processing Laboratories

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Room Layout Estimation based on Planar Semantic Segmentation

Room Layout Estimation based on Planar Semantic Segmentation

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2022 IEEE World Conference on Applied Intelligence and Computing, AIC 2022

作者： Huang, Shizhong Miao, Jun Yao, Hui Zheng, Yilin Chu, Jun Zhang, Guoxiang Nanchang Hangkong University School of Aeronautical Manufacturing Engineering Nanchang330063 China Nanchang Hangkong University Department of Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang330063 China University of California MercedCA95343 United States

ISBN: (数字)9781665479882

ISBN: (纸本)9781509050017

Layout estimation is one of fundamental tasks for understanding indoor scenes. In a single image of indoor scenes, key information, such as key points and boundaries for inferring the layout is often severely occluded or completely invisible. In this paper, a novel end-to-end deep learning network, which is extended from Xception module, is constructed for segmentation of semantic planes to infer the room layout structure. Moreover, the network contains an optimized depth wise separable convolution (DSC) in Xception module and a reinforced spatial attention module (SAM), so that it obtained good learning ability in severely occluded areas. Tests on public datasets show that the proposed network has a lower pixel error (PE) and higher generalization compared with existing algorithms. Ablation experiments prove that optimized DSC and SAM in the network are effective in improving the accuracy of layout estimation. © 2022 IEEE.

关键词： Semantic Segmentation

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Two-band Infrared Data Fusion Method Based-on Fractal Dimension

Two-band Infrared Data Fusion Method Based-on Fractal Dimens...

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2005 International Conference on Neural Networks and Brain(ICNN&B’05)

作者： Yuqiu Sun Jinwen Tian Jian Liu Image Processing and Intelligence Control Key Laboratory of State Educational Ministry Institute of Pattern Recognition and Artificial IntelligenceHuazhong University of Science and Technology School of information and mathematics Yangtze University Dept.of Electronic Information and Enginery of Huazhong University of Science and Technology

ISBN: (纸本)0780394224

＜正＞Researches indicate that the gray-scale images mapped from most nature objects accord to the fractal Brown stochastic field which foundation is self-similarity,an image is self-similarity means the image is made up of copies of itself in a reduced *** fractal dimension can quantificational depict the fractal character and the property of an image. Therefore,a new method based-on fractal dimension to fuse mid-wave infrared images and long-wave infrared images is presented in the ***,images are decomposed into different scale using wavelet ***,the fractal dimensions of sub-images of original images are computed,and then take them as weight to fuse sub-images at the same level according to different ***,reconstruct the fused images by wavelet inverse *** experimental results imply that the method can effectively preserve the information of source images with a high contrast and may be very practical.

关键词： Fractals Infrared imaging Artificial intelligence Fuses Educational technology image reconstruction Layout Infrared detectors Intelligent control Process control

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Unifying Gradients to Improve Real-World Robustness for Deep Networks

arXiv

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arXiv 2022年

作者： Wu, Yingwen Chen, Sizhe Fang, Kun Huang, Xiaolin Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University 800 Dongchuan Road Shanghai China Institute of Image Processing and Pattern Recognition The MOE Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University 800 Dongchuan Road Shanghai China

The wide application of deep neural networks (DNNs) demands an increasing amount of attention to their real-world robustness, i.e., whether a DNN resists black-box adversarial attacks, among which score-based query attacks (SQAs) are most threatening since they can effectively hurt a victim network with the only access to model outputs. Defending against SQAs requires a slight but artful variation of outputs due to the service purpose for users, who share the same output information with SQAs. In this paper, we propose a real-world defense by Unifying Gradients (UniG) of different data so that SQAs could only probe a much weaker attack direction that is similar for different samples. Since such universal attack perturbations have been validated as less aggressive than the input-specific perturbations, UniG protects real-world DNNs by indicating attackers a twisted and less informative attack direction. We implement UniG efficiently by a Hadamard product module which is plug-and-play. According to extensive experiments on 5 SQAs, 2 adaptive attacks and 7 defense baselines, UniG significantly improves real-world robustness without hurting clean accuracy on CIFAR10 and imageNet. For instance, UniG maintains a model of 77.80% accuracy under 2500-query Square attack while the state-of-the-art adversarially-trained model only has 67.34% on CIFAR10. Simultaneously, UniG outperforms all compared baselines in terms of clean accuracy and achieves the smallest modification of the model output. The code is released at https://***/snowien/UniG-pytorch. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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Objective resolution measurement in single particle reconstructions based on a new spectral signal-to-noise ratio estimation

Objective resolution measurement in single particle reconstr...

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第28届中国控制与决策会议

作者： Shuai Wang Biao Zhang Hong-Bin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China

ISBN: (纸本)9781467397155

The resolution measurement of 3D reconstructed density map in single particle reconstruction is an important and still an open *** this paper,we propose a new protocol to measure the resolution just from the reconstructed density *** approach estimates spectral signal-to-noise ratio(SSNR) of 3D reconstructed map by computing the ratio of signal power to noise power in frequency *** power distributions of signal and noise are estimated from structure particle region and surrounding region segmented by applying a mask *** proposed protocol of calculating SSNR,which we term mask-SSNR(mSSNR),is independent of the reconstruction algorithms and can be used for density maps reconstructed with any reconstruction ***,the mSSNR neither needs to split the dataset into halves like the Fourier shell correlation(FSC) approach,nor any original images or intermediate data like other SSNR calculation methods in this *** mSSNR provides a direct calculation of SSNR based on its original definition,and is proven to be a better approach.

关键词： Electron microscopy Resolution measurement Spectral signal-to-noise ratio Signal processing

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Semi-supervised Fisher-Markov Selector via Manifold Regularization

Semi-supervised Fisher-Markov Selector via Manifold Regulari...

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2012 International Conference on Applied Informatics and Communication(ICAIC 2012)

作者： Dong Chen Lin Zhu JieYang Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China

Feature subset selection is an important approach to deal with high-dimensional data. But selecting the best subset of data is NP hard. So most of feature selection methods cannot handle high-dimensional data efficiently, or they can only obtain local optimum instead of global optimum. In these cases, when the data consist of both labeled and unlabeled data, semi-supervised feature selection can make full use of data information. In this paper, we introduce a novel semi-supervised feature selection algorithm, which is a filter method based on Fisher-Markov selector, thus ours can achieve global optimum and computational efficiency under certain kernels.

关键词： Feature selection, Dimensionality reduction, Manifold regularization, Semi-supervised learning

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A statistical approach to contour extraction based on quantum mechanics

A statistical approach to contour extraction based on quantu...

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Medical Imaging 2009 - image processing

作者： Sun, Yangguang Lan, Tian Fu, Xiaowei Ding, Mingyue Institute of Pattern Recognition and Artificial Intelligence Image Processing and Intelligence Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan 430074 China School of Life Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780819475107

Contour extraction is a key issue in many medical applications. A novel statistical approach based on quantum mechanics to extract contour of the interested object of medical images was proposed in this paper. The natures of quantum statistical concepts such as the quantum discontinuity and the wave function correspond to the discrete and gray possibility of an image respectively. Contour extraction will be performed by the quantum particle movement, where the particle will be moved forward to the position with high probability density edges in image potential field. Experimental results with medical images demonstrated that the proposed approach has the capability to extract contours with arbitrary initialization and handle topology changes as well as both the inner and outer contours by a single initialization. Keywords: Statistical approach, contour extraction, path integral, quantum mechanics. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Medical applications

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image Representation via Sub-dictionary based Sparse Coding

Image Representation via Sub-dictionary based Sparse Coding

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International Joint Conference on Neural Networks

作者： Bingxin Xu Qian Yin Ping Guo Hongzhe Liu Beijing Key Laboratory of Information Service Engineering Beijing Union University Image Processing and Pattern Recognition Laboratory Beijing Normal University

ISBN: (纸本)9781509006212

In this paper, a sub-dictionary based sparse coding method is proposed for image representation. The novel sparse coding method substitutes a new regularization item for L1-norm in the sparse representation model. The proposed sparse coding method involves a series of sub-dictionaries. Each sub-dictionary contains all the training samples except for those from one particular category. For the test sample to be represented, all the sub-dictionaries should linearly represent it apart from the one that does not contain samples from that label, and this sub-dictionary is called irrelevant sub-dictionary. This new regularization item restricts the sparsity of each sub-dictionary's residual, and this restriction is helpful for classification. The experimental results demonstrate that the proposed method is superior to the previous related sparse representation based classification.

关键词： image representation Sparse coding sparse representation-based classification Linearly

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